In the second half of the nineteenth century, local leaders around the Qing empire attempted to rebuild in the aftermath of domestic rebellion and imperialist aggression. At the same time, the enthronement of a series of children brought the question of reconstruction into the heart of the capital. Chinese scholars, Manchu and Mongolian officials, and writers in the press all competed to have their ideas included in the education of young rulers. Each group hoped to use the power of the emperor—both his functional role within the bureaucracy and his symbolic role as an exemplar for the people—to promote reform. Daniel Barish explores debates surrounding the education of the final three Qing emperors, showing how imperial curricula became proxy battles for divergent visions of how to restabilize the country. He sheds light on the efforts of rival figures, who drew on China’s dynastic history, Manchu traditions, and the statecraft tools of imperial powers as they sought to remake the state. Barish traces how court education reflected arguments over the introduction of Western learning, the fate of the Manchu Way, the place of women in society, notions of constitutionalism, and emergent conceptions of national identity. He emphasizes how changing ideas of education intersected with a push for a renewed imperial center and national unity, helping create a model of rulership for postimperial regimes. Through the lens of the education of young emperors, Learning to Rule develops a new understanding of the late Qing era and the relationship between the monarchy and the nation in modern China.

Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.

Forget the 10,000 hour rule— what if it’s possible to learn the basics of any new skill in 20 hours or less? Take a moment to consider how many things you want to learn to do. What’s on your list? What’s holding you back from getting started? Are you worried about the time and effort it takes to acquire new skills—time you don’t have and effort you can’t spare? Research suggests it takes 10,000 hours to develop a new skill. In this nonstop world when will you ever find that much time and energy? To make matters worse, the early hours of prac­ticing something new are always the most frustrating. That’s why it’s difficult to learn how to speak a new language, play an instrument, hit a golf ball, or shoot great photos. It’s so much easier to watch TV or surf the web . . . In The First 20 Hours, Josh Kaufman offers a systematic approach to rapid skill acquisition— how to learn any new skill as quickly as possible. His method shows you how to deconstruct com­plex skills, maximize productive practice, and remove common learning barriers. By complet­ing just 20 hours of focused, deliberate practice you’ll go from knowing absolutely nothing to performing noticeably well. Kaufman personally field-tested the meth­ods in this book. You’ll have a front row seat as he develops a personal yoga practice, writes his own web-based computer programs, teaches himself to touch type on a nonstandard key­board, explores the oldest and most complex board game in history, picks up the ukulele, and learns how to windsurf. Here are a few of the sim­ple techniques he teaches: Define your target performance level: Fig­ure out what your desired level of skill looks like, what you’re trying to achieve, and what you’ll be able to do when you’re done. The more specific, the better. Deconstruct the skill: Most of the things we think of as skills are actually bundles of smaller subskills. If you break down the subcompo­nents, it’s easier to figure out which ones are most important and practice those first. Eliminate barriers to practice: Removing common distractions and unnecessary effort makes it much easier to sit down and focus on deliberate practice. Create fast feedback loops: Getting accu­rate, real-time information about how well you’re performing during practice makes it much easier to improve. Whether you want to paint a portrait, launch a start-up, fly an airplane, or juggle flaming chain­saws, The First 20 Hours will help you pick up the basics of any skill in record time . . . and have more fun along the way.

The rules keep getting in the way of Noodle's fun. Rules for this, rules for that. There are so many rules! Can Noodle be convinced that rules are meant to help, not harm him?

The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.

CasebookPlus Hardbound - New, hardbound print book includes lifetime digital access to an eBook, with the ability to highlight and take notes, and 12-month access to a digital Learning Library that includes self-assessment quizzes tied to this book, online videos, interactive trial simulations, leading study aids, an outline starter, and Gilbert Law Dictionary.

This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.